Magnetic resonance (MR) is a key medical imaging modality that has critical roles in both patient care and medical research. MR scanner installations, however, are expensive. Another major limitation to MR imaging is the comparatively long image acquisition times, especially when compared to other modalities like computerized tomography. Lengthy acquisition times make MR less patient friendly and increase the per patient examination cost. MR-based compressed sensing (CS) methods seek to leverage the implicit sparsity of medical images [1]}, potentially allowing for significant k-space undersampling during acquisition, and by consequence, reducing examination times.
